Prob.norm-methods: Probabilistic normalization of a graph

In NetPreProc: Network Pre-Processing and Normalization

Probabilistic normalization of a graph

Description

Method to compute the transition probability matrix of network. A network matrix is normalized by dividing each entry W_{ij} by the the sum of elements of row i In other words if D is a diagonal matrix such that D_{ii} = ∑_j W_{ij} then the normalize matrix is:

W_{norm} = D^{-1} W

Usage

Prob.norm(W)

Arguments

W	an object representing the graph to be normalized

Value

The normalized transition probability matrix of network

Methods

signature(W = "graph")

an object of the virtual class graph (hence including objects of class graphAM and graphNEL from the package graph)

signature(W = "matrix")

a matrix representing the adjacency matrix of the graph

Examples

library(bionetdata);
# making transition prob matrix for a drug-drug similarity network
data(DD.chem.data);
W <- Prob.norm(DD.chem.data);
# the same using an object of class graphAM and of class graphNEL

g <- new("graphAM", adjMat=DD.chem.data, values=list(weight=DD.chem.data));
Wg <- Prob.norm(g);
g2 <- as(g, "graphNEL");
Wg2 <- Prob.norm(g2);

NetPreProc documentation built on Sept. 19, 2022, 5:06 p.m.